Method load_point_cloud_grid

benchmark/benchmark/core/loader.py:401–549

Load colored point cloud in world coordinates. This method loads colored 3D point clouds in world coordinate system from two sources: (1) If load_point_list() returns point cloud data, use the point cloud xyz directly (2) If load_point_list() returns no point cloud, use load

(
        self,
        confidence_threshold: float = 0.0,
        use_aoi_mask: bool = False
    )

        return None

    def load_point_cloud_grid(
        self,
        confidence_threshold: float = 0.0,
        use_aoi_mask: bool = False
    ) -> Tuple[np.ndarray, np.ndarray]:
        """Load colored point cloud in world coordinates.

        This method loads colored 3D point clouds in world coordinate system from two sources:
        (1) If load_point_list() returns point cloud data, use the point cloud xyz directly
        (2) If load_point_list() returns no point cloud, use load_depth_list() to get depth maps,
            combine with load_intrinsics() camera intrinsics to backproject depth into xyz points,
            and use load_trajectory() camera poses to transform points from camera to world coords.
        Then combine with load_rgb_list() RGB images to get point cloud color information.

        Args:
            confidence_threshold: Percentile threshold (0-1) to filter points based on confidence maps.
                                If > 0, only points with confidence above this percentile are kept.
                                Default 0.0 means no filtering.
            use_aoi_mask: If True, apply area-of-interest masks from mask/ directory
                         (checks data_dir/mask/ first, then sibling gt/mask/).
                         Only pixels where the mask is non-zero are kept.
                         Default False. Typically only enabled during evaluation.

        Returns:
            xyzrgb: DxHxWx6 float32 array (xyz in meters, rgb in [0,1])
            mask: DxHxW boolean mask where valid points exist (filtered by confidence if threshold > 0)
        """
        N = self.get_num_frames()
        if N == 0:
            raise ValueError("No frames found")

        point_list = self.load_points_list()
        use_point_cloud = (point_list is not None and all(p is not None for p in point_list))

        rgb_list = self.load_rgb_list()

        traj_array = self.load_trajectory()
        if traj_array is None:
            raise FileNotFoundError(f"Trajectory file not found in {self.artifact}")

        if not use_point_cloud:
            depth_list = self.load_depth_list()
            intrinsics_array = self.load_intrinsics()

            if depth_list is None:
                raise FileNotFoundError(f"Depth directory not found in {self.artifact}")
            if intrinsics_array is None:
                raise FileNotFoundError(f"Intrinsics file not found in {self.artifact}")
            if any(d is None for d in depth_list):
                raise ValueError("Some depth maps are missing")

        H, W = rgb_list[0].shape[:2]

        xyzrgb = np.zeros((N, H, W, 6), dtype=np.float32)
        mask = np.zeros((N, H, W), dtype=bool)

        if use_point_cloud:
            for i in range(N):